Community Flood Resilience Categorization Framework

Abstract

Coupled with climate change, the expansive developments of urban areas are causing a significant increase in flood-related disasters worldwide. However, most flood risk analysis and categorization efforts have been focused on the hydrologic features of flood hazards (e.g., inundation depth, extent, and duration), rarely considering the resulting long-term losses and recovery time (i.e., the community's flood resilience). This paper aims at developing a data-driven community flood resilience categorization framework that can be utilized for the development of realistic disaster management strategies and proactive risk mitigation measures to better protect urban centers from future catastrophic flood events. This approach considers key resilience goals such as the robustness of the exposed community and its recovery rapidity. Such categorization that calls on the two resilience means, namely resourcefulness and redundancy, can empower decision makers to learn from past events and guide future resilience strategies. To demonstrate the applicability of the developed framework, a data-driven framework was applied on historical mainland flood disaster records collected by the US National Weather Services between 1996 and 2019. Descriptive analysis was conducted to identify the features of this dataset as well as the interdependence between the different variables considered. To further demonstrate the utilization of the developed data-driven framework, a spatial analysis was conducted to quantify community flood resilience across different counties within the affected states. Beyond the work presented in this paper, the developed framework lays the foundation to adopt data driven approaches for disasters prediction to guide proactive risk mitigation measures and develop community resilience management insights.